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research-article

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

Published: 01 November 2024 Publication History

Abstract

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

Highlights

An abnormal recognition-assisted network links ECG delineation and disease diagnosis.
Our onset-offset aware loss improves waveform boundary detection, reducing misdiagnosis.
Our method shows favorable results on both wearable and hospital ECG datasets.

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              Published In

              cover image Artificial Intelligence in Medicine
              Artificial Intelligence in Medicine  Volume 157, Issue C
              Nov 2024
              404 pages

              Publisher

              Elsevier Science Publishers Ltd.

              United Kingdom

              Publication History

              Published: 01 November 2024

              Author Tags

              1. Wearable ECG
              2. Pathological ECG delineation
              3. Onset-offset aware loss
              4. Abnormal recognition-assisted network

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